Precise representations of 3D faces are beneficial to various computer vision and graphics applications. Due to the data discretization and model linearity, however, it remains challenging to capture accurate identity and expression clues in current studies. This paper presents a novel 3D morphable face model, namely ImFace, to learn a nonlinear and continuous space with implicit neural representations. It builds two explicitly disentangled deformation fields to model complex shapes associated with identities and expressions, respectively, and designs an improved learning strategy to extend embeddings of expressions to allow more diverse changes. We further introduce a Neural Blend-Field to learn sophisticated details by adaptively blending a series of local fields. In addition to ImFace, an effective preprocessing pipeline is proposed to address the issue of watertight input requirement in implicit representations, enabling them to work with common facial surfaces for the first time. Extensive experiments are performed to demonstrate the superiority of ImFace.

本文提出了一种新的3D可塑人脸模型ImFace，能够学习一种非线性和连续空间，用于捕捉具有特定身份和表情的复杂形状。除了ImFace，还提出了有效的预处理管道，以解决隐式表示中watertight输入要求的问题，这使得它们可以与常见的人脸表面一起使用。通过大量实验，论文证明了ImFace模型的优越性。

ImFace：一种具有隐式神经表示的非线性3D可塑性面部模型